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- W4385474559 abstract "The task of state estimation faces a major challenge due to the inherent lack of real-time observability, as certain measurements can only be acquired with a delay. As a result, power systems are essentially unobservable in real time, indicating the existence of multiple states that result in identical values for the available measurements. Certain existing approaches utilize historical data to infer the relationship between real-time available measurements and the state. Other learning-based methods aim at generating the pseudo-measurements required to make the system observable. Our paper presents a methodology that utilizes the outcome of an unobservable state estimator to exploit information on the joint probability distribution between real-time available measurements and delayed ones. Through numerical simulations conducted on a realistic electricity network with insufficient real-time measurements, the proposed procedure showcases superior performance compared to existing state forecasting approaches and those relying on inferred pseudo-measurements." @default.
- W4385474559 created "2023-08-02" @default.
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- W4385474559 date "2023-07-31" @default.
- W4385474559 modified "2023-09-26" @default.
- W4385474559 title "Learning-based Improvement in State Estimation for Unobservable Systems" @default.
- W4385474559 doi "https://doi.org/10.48550/arxiv.2307.16822" @default.
- W4385474559 hasPublicationYear "2023" @default.
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